Original Article

• 2024)

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• 2023)

  • Mineharu Y, Takagi Y, Koizumi A, Morimoto T, Funaki T, Hishikawa T, Araki Y, Hasegawa H, Takahashi JC, Kuroda S, Houkin K, Miyamoto S; SUPRA Japan Study Group. Posterior cerebral artery involvement in unilateral moyamoya disease is exclusively ipsilateral and influenced by RNF213 mutation gene dose: The SUPRA Japan study: PCA involvement in unilateral moyamoya.,Journal of Stroke Cerebrovasc. Dis., 2023;33:107513.　2023/12/22 doi/10.1016/j.jstrokecerebrovasdis.2023.107513
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• 2022)

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  • Sato N, Uchino E, Kojima R, Sakuragi M, Hiragi S, Minamiguchi S, Haga H, Yokoi H, Yanagita M, Okuno Y , Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney international reports ,6(9), 2445－2454 , 2021/9 , DOI: 10.1016/j.ekir.2021.06.008
  • Miyaguchi I,Sato M,Kashima A,Nakagawa H,Kokabu Y, M Biao,Matsumoto S,Tokuhisa A, Ohta M& Ikeguchi M, Machine learning to estimate the local quality of protein crystal structures,Scientific Report,11, 23599 , 2021,https://doi.org/10.1038/s41598-021-02948-y
  • Mai Adachi Nakazawa, Yoshinori Tamada, Yoshihisa Tanaka, Marie Ikeguchi, Kako Higashihara & Yasushi Okuno,Novel cancer subtyping method based on patient-specific gene regulatory network,Scientific Reports volume 11, Article number: 23653, 2021, DOI:https://doi.org/10.1038/s41598-021-02394-w
  • Nakamura H, Takami H, Yanagisawa T, Kumabe T, Fujimaki T, Arakawa Y, Karasawa K, Terashima K, Yokoo H, Fukuoka K, Sonoda Y, Sakurada K, Mineharu Y, Soejima T, Fujii M, Shinojima N, Hara J, Yamasaki K, Fujimura J, Takahashi M, Suzuki T, Sato I, Nishikawa R, Sugiyama K, guideline committee in The Japan Society for Neuro-Oncology (JSNO) Task Force on Central Nervous System Germ Cell Tumors. The Japan Society for Neuro-Oncology Guideline on the Diagnosis and Treatment of Central Nervous System Germ Cell Tumors. Neuro. Oncol. noab242 , 2021, https://doi.org/10.1093/neuonc/noab242
  • Arakawa Y, Sasaki K, Mineharu Y, Uto M, Mizowaki T, Mizusawa J, Sekino Y, Ono T, Aoyama H, Satomi K, et al. A randomized phase III study of short-course radiotherapy combined with Temozolomide in elderly patients with newly diagnosed glioblastoma; Japan clinical oncology group study JCOG1910 (AgedGlio-PIII). BMC Cancer. 21:1105., 2021, https://doi.org/10.1186/s12885-021-08834-0
  • Makino Y, Arakawa Y, Yoshioka E, Shofuda T, Minamiguchi S, Kawauchi T, Tanji M, Kanematsu D, Nonaka M, Okita Y, Kodama Y, Mano M, Hirose T, Mineharu Y, Miyamoto S, Kanemura Y. Infrequent RAS mutation is not associated with specific histological phenotype in gliomas. BMC Cancer. ,21:1025. , 2021,https://doi.org/10.1186/s12885-021-08733-4
  • Mineharu Y, Takagi Y, Koizumi A, Morimoto T, Funaki T, Hishikawa T, Araki Y, Hasegawa H, Takahashi JC, Kuroda S, Houkin K, and Miyamoto S: on behalf of the SUPRA Japan Study Group. Genetic and non-genetic factors for contralateral progression of unilateral moyamoya disease: the first report from the SUPRA Japan Study Group, J Neurosurg, 2021,https://doi.org/10.3171/2021.3.JNS203913
  • Terayama K, Sumita M, Katouda M, Tsuda K, Okuno Y, Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization. Journal of Chemical Theory and Computation, 17:5419–5427, 2021,DOI:https://doi.org/10.1021/acs.jctc.1c00301
  • Matsumoto S, Taniguchi-Tamura H, Araki M, Kawamura T, Miyamoto R, Tsuda C, Shima F, Kumasaka T, Okuno Y*, Kataoka T*, Oncogenic mutations Q61L and Q61H confer active form-like structural features to the inactive state (state 1) conformation of H-Ras protein. Biochemical and Biophysical Research Communications. 565:85-90, 2021,https://doi.org/10.1016/j.bbrc.2021.05.084
  • Iwata H, Kojima R, Okuno Y*, AIM in Pharmacology and Drug Discovery. Artificial Intelligence in Medicine. 1-9, 2021,https://doi.org/10.1007/978-3-030-58080-3_145-1
  • Uchino E,Sato N, Okuno Y, Artificial Intelligence in Predicting Kidney Function and Acute Kidney Injury, Artificial Intelligence in Medicine,pp 1-17,2021/8,https://doi.org/10.1007/978-3-030-58080-3_270-1
  • Sato N,Uchino E,Okuno Y. Artificial Intelligence in Kidney Pathology. In: Lidströmer N., Ashrafian H. (eds) Artificial Intelligence in Medicine. Springer, Cham.pp 1-11,2021, https://doi.org/10.1007/978-3-030-58080-3_181-1
  • Tsutsui T, Arakawa Y*, Makino Y, Kataoka H, Mineharu Y, Minamiguchi S, Hirose T, Nobusawa S, Nakano Y, Ichimura K, Haga H, Miyamoto S. Spinal cord astroblastoma with EWSR1-BEND2 fusion classified as HGNET-MN1 by methylation classification: a case report　Brain Tumor Pathology volume 38, pages283–289 ,2021,https://doi.org/10.1007/s10014-021-00412-3
  • Mineharu Y, Miyamoto S. RNF213 and GUCY1A3 in Moyamoya Disease: Key Regulators of Metabolism, Inflammation, and Vascular Stability. Front Neurol ,12:1–12. ,2021,https://doi.org/10.3389/fneur.2021.687088
  • Yamawaki R*, Nankaku M, Umaba C, Ueda M, Liang N, Mineharu Y, Yamao Y, Ikeguchi R, Matsuda S, Miyamoto S, et al. Assessment of neurocognitive function in association with WHO grades in gliomas. Clin Neurol Neurosurg ,208:106824. ,2021,https://doi.org/10.1016/j.clineuro.2021.106824
  • Kamada M, Takagi A, KojimaR, TanakaY, Nakatsui M, Tanabe N, Hirata M, Yoshida T, Okuno Y , Network-based pathogenicity prediction for variants of uncertain significance. bioRxiv, 2021 ,https://doi.org/10.1016/j.bbrc.2021.05.084
  • Makino Y, Arakawa Y, Yoshioka E, Shofuda T, Kawauchi T, Terada Y, Tanji M, Kanematsu D, Mineharu Y, Miyamoto S, et al. Prognostic stratification for IDH wild type lower grade astrocytoma by Sanger sequencing and copy number alteration analysis with MLPA. Sci Rep volume 11, Article number: 14408,1–12., 2021 , https://doi.org/10.1038/s41598-021-93937-8
  • Kamada M, Okuno Y*, AIM in genomic basis of medicine: applications. Artificial Intelligence in Medicine. 1-10, 2021 , https://doi.org/10.1007/978-3-030-58080-3_145-1
  • Ma B, Terayama K*, Matsumoto S, Isaka Y, Sasakura Y, Iwata H, Araki M, Okuno Y*, Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. Journal of Chemical Information and Modeling. 61(7): 3304–3313, 2021,doi.org/10.1021/acs.jcim.1c00679
  • Shinno K, Arakawa Y, Minamiguchi S, Terada Y, Tanji M, Mineharu Y, Kikuchi T, Haga H, Miyamoto S. Papillary glioneuronal tumor growing slowly for 26 years: illustrative case. J Neurosurg Case Lessons  2:5–8. https://doi.org/10.3171/CASE21266
  • Sato N, Uchino E, Kojima R, Hiragi S, Yanagita M, Okuno Y*, Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data. Computer Methods and Programs in Biomedicine. 206:106129, 2021,https://doi.org/10.1016/j.cmpb.2021.106129
  • Chiba S, Lim KRQ, Sheri N, Anwar S, Erkut E, Shah MH, Aslesh T, Woo S, Sheikh O, Maruyama R, Takano H, Kunitake K, Duddy W, Okuno Y*, Aoki Y*, Yokota T*, eSkip-Finder: a machine learning-based web application and database to identify the optimal sequences of antisense oligonucleotides for exon skipping. Nucleic Acids Research. gkab442, 2021,https://doi.org/10.1093/nar/gkab442
  • Mizota T, Hamada M, Shiraki A, Kikuchi T, Mineharu Y, Yamao Y, Hattori EY, Yonezawa A, Furukawa K, Arakawa Y. Factors associated with somnolence during brain function mapping in awake craniotomy. J. Clin. Neurosci. ,89:349–353., 2021, https://doi.org/10.1016/j.jocn.2021.05.050
  • Tanaka Y, Higashihara K, Nakazawa M.A, Yamashita F, Tamada Y & Okuno Y. “Dynamic changes in gene-to-gene regulatory networks in response to SARS-CoV-2 infection” Sci Rep. ,11:11241. , 2021,https://doi.org/10.1038/s41598-021-90556-1
  • Nojima S, Terayama K, Shimoura S, Hijiki S, Nonomura N, Morii E, Okuno Y , Fujita K* A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Cancer Cytopathology ,6(9), 2445－2454 , 2021/9 , DOI: 10.1002/cncy.22443
  • Nakamura K, Kojima R, Uchino E, Ono K, Yanagita M, Murashita K, Itoh K, Nakaji S & Okuno Y. “Health improvement framework for actionable treatment planning using a surrogate Bayesian model” Nat Commun. ,12:3088. , 2021, https://doi.org/10.1038/s41467-021-23319-1
  • Araki M, Matsumoto S, Bekker GJ, Isaka Y, Sagae Y, Kamiya N & Okuno Y. “Exploring ligand binding pathways on proteins using hypersound-accelerated molecular dynamics” Nat Commun. .12:2793. , 2021,https://doi.org/10.1038/s41467-021-23157-1
  • Matsumoto S, Ishida S, Araki M, Kato T, Terayama K, Okuno Y. “Extraction of protein dynamics information from cryo-EM maps using deep learning” Nat Mach Intell. ,3:153-160. , 2021,https://doi.org/10.1038/s42256-020-00290-y
  • Iwata H, Matsuo T, Mamada H, Motomura T, Matsushita M, Fujiwara T, Maeda K, Handa K. “Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning” Journal of Pharmaceutical Sciences. , 2021,https://doi.org/10.1016/j.xphs.2021.01.020
• 2020)

  • Kawaguchi C, Shintani N, Hayata-Takano A, Hatanaka M, Kuromi A, Nakamura R, Yamano Y, Shintani Y, Nagai K, Tsuchiya S, Sugimoto Y, Ichikawa A, Okuno Y, Urade Y, Hirai H, Nagata KY, Nakamura M, Narumiya S, Nakazawa T, Kasai A, Ago Y, Takuma K, Baba A, Hashimoto H. “Lipocalin-type prostaglandin D synthase regulates light-induced phase advance of the central circadian rhythm in mice” Commun Biol. 2020;3(1):557. Available from: http://doi.org/10.1038/s42003-020-01281-w
  • Hagihara H, Ienaga N, Enomoto D, Takahata S, Ishihara H, Noda H, Tsuda K, Terayama K. “Computer Vision–Based Approach for Quantifying Occupational Therapists’ Qualitative Evaluations of Postural Control” Occupational Therapy International. 2020:8542191. Available from: http://doi.org/10.1155/2020/8542191
  • Shuntaro Chiba, Aki Tanabe, Makoto Nakakido, Yasushi Okuno, Kouhei Tsumoto, Masateru Ohta. “Structure-based design and discovery of novel anti-tissue factor antibodies with cooperative double-point mutations, using interaction analysis” Sci Rep. 2020;10:17590. Available from: http://doi.org/10.1038/s41598-020-74545-4
  • Hiroaki Iwata, Naoto Kanda, Mitsugu Araki, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Discovery of Natural TRPA1 Activators through Pharmacophore-based Virtual Screening and a Biological Assay” Bioorganic & Medicinal Chemistry Letters. 2020:127639. Available from: https://doi.org/10.1016/j.bmcl.2020.127639
  • Mitsugu Araki, Naoto Kanda, Hiroaki Iwata, Yukari Sagae, Katsuyoshi Masuda, Yasushi Okuno. “Identification of a New Class of Non-Electrophilic TRPA1 Agonists by a Structure-Based Virtual Screening Approach. Bioorganic & Medicinal Chemistry Letters. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
  • Gert-Jan Bekker, Mitsugu Araki, Kanji Oshima, Yasushi Okuno, Narutoshi Kamiya. “Exhaustive Search of the Configurational Space of Heat-Shock Protein 90 With Its Inhibitor by Multicanonical Molecular Dynamics Based Dynamic Docking” Journal of Computational Chemistry. 2020;41(17):1606-1615. Available from: https://doi.org/10.1002/jcc.26203
  • Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, Koji Tsuda. “CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration” Journal of Cheminformatics. 2020;12:52. Available from: https://doi.org/10.1186/s13321-020-00452-5
  • Uchino E, Suzuki K, Sato N, Kojima R, Tamada Y, Hiragi S, Yokoi H, Yugami N, Minamiguchi S, Haga H, Yanagita M, Okuno Y. “Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach” Int. J. Med. Inform. 2020;141:104231. Available from: https://doi.org/10.1016/j.ijmedinf.2020.104231
  • Matsumoto S, Araki M, Isaka Y, Ono F, Hirohashi K, Ohashi S, Muto M, Okuno Y. “E487K-Induced Disorder in Functionally Relevant Dynamics of Mitochondrial Aldehyde Dehydrogenase 2” Biophys. J. 2020 Jul 10; Available from: https://doi.org/10.1016/j.bpj.2020.07.002
  • Araki M, Kanegawa N, Iwata H, Sagae Y, Ito K, Masuda K, Okuno Y. “Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides” Heliyon. 2020;6(6):e04227. Available from: https://doi.org/10.1016/j.heliyon.2020.e04227
  • Kato K, Masuda T, Watanabe C, Miyagawa N, Mizuochi H, Nagase S, Kamisaka K, Oshima K, Ono S, Ueda H, Tokuhisa A, Kanada R, Ohta M, Ikeguchi M, Okuno Y, Fukuzawa K, Honma T. ” High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning” J. Chem. Inf. Model. 2020. Available from: https://doi.org/10.1021/acs.jcim.0c00273
  • Tokuhisa A, Kanada R, Chiba S, Terayama K, Isaka Y, Ma B, Kamiya N, Okuno Y. “Coarse-Grained Diffraction Template Matching Model to Retrieve Multiconformational Models for Biomolecule Structures from Noisy Diffraction Patterns” J. Chem. Inf. Model. 2020;60(6):2803–2818. Available from: https://doi.org/10.1021/acs.jcim.0c00131
  • Yamanaka M, Iwata H, Masuda K, Araki M, Okuno Y, Okamura M, Koiwa J, Tanaka T. “A novel orexin antagonist from a natural plant was discovered using zebrafish behavioural analysis” Eur Rev Med Pharmacol Sci. 2020;24(9):5127-5139. Available from: https://doi.org/10.26355/eurrev_202005_21207
  • Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y. “kGCN: a graph-based deep learning framework for chemical structures” J Cheminform [Internet]. 2020 May 12;12(1):32. Available from: https://doi.org/10.1186/s13321-020-00435-6
  • Masuda N, Murakami K, Kita Y, Hamada A, Kamada M, Teramoto Y, Sakatani T, Matsumoto K, Sano T, Saito R, Okuno Y, Ogawa O, Kobayashi T. “Trp53 mutation in Krt5-expressing basal cells facilitates the development of basal squamous-like invasive bladder cancer in the chemical carcinogenesis of mouse bladder” Am J Pathol. 2020 Apr 24. Available from: https://doi.org/10.1016/j.ajpath.2020.04.005
  • Kanada R, Tokuhisa A, Tsuda K, Okuno Y, Terayama K. “Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning” Biomolecules. 2020;10(3):482. Available from: https://doi.org/10.3390/biom10030482
  • Saito Y, Koya J, Araki M, Kogure Y, Shingaki S, Tabata M, McClure M, Yoshifuji K, Matsumoto S, Isaka Y, Tanaka H, Kanai T, Miyano S, Shiraishi Y, Okuno Y, Kataoka K. . “Landscape and function of multiple mutations within individual oncogenes” Nature. 2020. Available from: https://doi.org/10.1038/s41586-020-2175-2
  • Araki M, Kanda M, Iwata H, Sagae Y, Masuda K, Okuno Y. “Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach” Bioorg Med Chem Lett. 2020;30(11):127142. Available from: https://doi.org/10.1016/j.bmcl.2020.127142
  • Sato N, Kakuta M, Uchino E, Hasegawa T, Kojima R, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Imoto S, Nakaji S, Murashita K, Yanagita M,Okuno Y. “The relationship between cigarette smoking and the tongue microbiome in an East Asian population” J. Oral Microbiol [Internet]. 2020;12(1):1–9. Available from: https://doi.org/10.1080/20002297.2020.1742527
  • Sato N, Kakuta M, Hasegawa T, Yamaguchi R, Uchino E, Kobayashi W, Sawada K, Tamura Y, Tokuda I, Murashita K, Nakaji S, Imoto S, Yanagita M, Okuno Y. “Metagenomic analysis of bacterial species in tongue microbiome of current and never smokers” npj Biofilms Microbiomes [Internet]. 2020;6(1):1–9. Available from: http://dx.doi.org/10.1038/s41522-020-0121-6
  • Ono F, Chiba S, Isaka Y, Matsumoto S, Ma B, Katayama R, Araki M, Okuno Y. “Improvement in predicting drug sensitivity changes associated with protein mutations using a molecular dynamics based alchemical mutation method” Sci Rep [Internet]. 2020;10(1):2161. Available from: https://doi.org/10.1038/s41598-020-58877-9
  • Tanaka Y, Tamada Y, Ikeguchi M, Yamashita F, Okuno Y. “System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork”  Biomolecules. 2020;10(2):306. Available from:https://doi.org/10.3390/biom10020306
    
  • Hatae R, Chamoto K, Kim YH, Sonomura K, Taneishi K, Kawaguchi S, Yoshida H, Ozasa H, Sakamori Y, Akrami M, Fagarasan S, Masuda I, Okuno Y, Matsuda F, Hirai T, Honjo T. “Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy” JCI Insight. 2020;5(2):e133501. Aveilable from: https://doi.org/10.1172/jci.insight.133501
  • Kawai R, Chiba S, Okuwaki K, Kanada R, Doi H, Ono M, Mochizuki Y, Okuno Y. “Stabilization Mechanism for a Nonfibrillar Amyloid β Oligomer Based on Formation of a Hydrophobic Core Determined by Dissipative Particle Dynamics” ACS Chem Neurosci. 2020;11:385–394. Available from: https://doi.org/10.1021/acschemneuro.9b00602
• 2019)

  • Koshimizu H, Kojima R, Kario K, Okuno Y. “Prediction of blood pressure variability using deep neural networks” Int J Med Inform [Internet]. 2020;136(October 2019):104067. Available from: https://doi.org/10.1016/j.ijmedinf.2019.104067
  • 小島 諒介, 鈴木真知子, 荒木綾乃, 奥野恭史.「重度障害児（者）のコミュニケーション向上に向けたアイトラッカーを用いた視線分析」日本重症心身障害学会誌, Dec, 2019;44(3):615-623.
  • Ishida S, Terayama K, Kojima R, Takasu K, Okuno Y. “Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks” J Chem Inf Model 2019;59(12):5026-33. Available from: https://doi.org/10.1021/acs.jcim.9b00538
    
  • Kita Y, Hamada A, Saito R, Teramoto Y, Tanaka R, Takano K, Nakayama K, Murakami K, Matsumoto K, Akamatsu S, Yamasaki T, Inoue T, Tabata Y, Okuno Y, Ogawa O, Kobayashi T. “Systematic chemical screening identifies disulfiram as a repurposed drug that enhances sensitivity to cisplatin in bladder cancer: a summary of preclinical studies” Br J Cancer 2019;121(12):1027-38. Available from: https://doi.org/10.1038/s41416-019-0609-0
  • Kamada, M., Naktsui,M., Kojima,R., Nohara.S., Uchino,E., Tanishima,S., Sugiyama,M., Kosaki,K., Tokunaga,K., Mizokam,M., Okuno,Y.,”MGeND: an integrated database for Japanese clinical and genomic information” Hum Genome Var 2019;6:53. Available from: https://dx.doi.org/10.1038%2Fs41439-019-0084-4
    
  • Negami T, Araki M, Okuno Y, Terada T. Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs. Sci Rep [Internet]  2019;9(1):16586. Available from: http://dx.doi.org/10.1038/s41598-019-53120-6
  • Iwata H, Kojima R, Okuno Y. “An in Silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery” Mol Inform. 2020;39(1-2):1900096. Available from: https://doi.org/10.1002/minf.201900096
    
  • Tokuhisa A. “Characterization of X-ray diffraction intensity function from a biological molecule for single particle imaging” Biophys Physicobiol. 2019;16:430-43. Available from: https://doi.org/10.2142/biophysico.16.0_430
    
  • Chiba S, Okuno Y, Honma T, Ikeguchi M. “Force-field parametrization based on radial and energy distribution functions” J Comput Chem 2019;40(29):2577–85. Available from: https://doi.org/10.1002/jcc.26035
    
  • Quy,PN., Kanai, M., Fukuyama, K., Kou, T., Kondo, T., Yamamoto, Y., Matsubara, J., Hiroshima, A., Mochizuki, H., Sakuma, T., Kamada, M., Nakatsui, M., Eso, Y., Seno, H., Masui, T., Masui, K., Minamiguchi, S., Matsumoto, S., Muto, M.”Association between preanalytical factors and tumor mutational burden estimated by next-generation sequencing-based multiplex gene panel assay” The Oncologist. 2019;24(12):e1401-e1408. Available from: https://doi.org/10.1634/theoncologist.2018-0587
    
  • Ikemura, S., Yasuda, H., Matsumoto, S., Kamada, M., Hamamoto, J., Masuzawa, K., Kobayashi, K., Manabe, T., Arai, D., Nakachi, I., Kawada, I., Ishioka, K., Nakamura, M., Namkoong, H., Naoki, K., Ono, F., Araki, M., Kanada, R., Ma, B., Hayashi, Y., Mimaki, S., Yoh, K., Kobayashi, S., Kohno, T., Okuno, Y., Goto, K., Tsuchihara, K., and Soejima, K. “Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations” PNAS  2019;116(20):10025-10030. Available from: https://doi.org/10.1073/pnas.1819430116
    
  • Ikeda, A., Funakoshi, E., Araki, M., Ma, B., Karuo, Y., Tarui, A., Sato, K., Okuno, Y., Kawai, K., Omote, M. “Structural modification of indomethacin toward selective inhibition of COX-2 with a significant increase in van der Waals contributions” Bioorg Med Chem. 2019;27(9):1789-1794. Avalilable from: https://doi.org/10.1016/j.bmc.2019.03.021
    
  • Okada, K., Araki, M., Sakashita, T., Ma, B., Kanada, R., Yanagitani, N., Horiike, A., Koike, S., Oh-hara, T., Watanabe, K., Tamai, K., Maemondo, M., Nishio, M., Ishikawa, T., Okuno, Y., Fujita, N., Katayama, R. “Prediction of ALK mutations mediating ALK-TKIs resistance and drug re-purposing to overcome the resistance” EBioMedicine. 2019;41:105-119. Available from: https://doi.org/10.1016/j.ebiom.2019.01.019
  • Shiraishi, A., Okuda, T., Miyasaka, N., Osugi, T., Okuno, Y., Inoue, J., Satake, H. “Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides” PNAS. b2019;116(16):7847-7856. Available from: https://doi.org/10.1073/pnas.1816640116
    
  • Bekker, G.J., Araki, M., Oshima, K., Okuno, Y., Kamiya, N. “Dynamic Docking of a Medium-Sized Molecule to Its Receptor by Multicanonical MD Simulations” J. Phys. Chem. B. 2019;123(11):2479-2490. Available from: https://doi.org/10.1021/acs.jpcb.8b12419
    
  • Mizumoto, A., Ohashi, S., Kamada, M., Saito, T., Nakai, Y., Baba, K., Hirohashi, K., Mitani, Y., Kikuchi, O., Matsubara, J., Yamada, A., Takahashi, T., Lee, H., Okuno, Y., Kanai, M., and Muto, M. “Combination treatment with highly bioavailable curcumin and NQO1 inhibitor exhibits potent antitumor effects on esophageal squamous cell carcinoma” J Gastroenterol. 2019;54:687-698. Available from: https://doi.org/10.1007/s00535-019-01549-x
  • Yamada, K., Sato, H., Sakamaki, K., Kamada, M., Okuno, Y., Fukuishi, N., Furuta, K., and Tanaka, S. “Suppression of IgE-Independent Degranulation of Murine Connective Tissue-Type Mast cells by Dexamethasone” Cells. 2019;8(2):E112. Available from: https://doi.org/10.3390/cells8020112
    
  • Terayama, K., Tamura, R., Nose, Y., Hiramatsu, H., Hosono, H., Okuno, Y., Tsuda, K. “Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling” Phys Rev Mater, accepted on 26 Jan, 2019. Available from: https://doi.org/10.1103/PhysRevMaterials.3.033802
• 2018)

  • Kou T, Kana M, Kamada M, Nakatsui M, Matsumoto S, Okuno Y, Muto M. “A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection” Methods in molecular biology (Clifton, N.J.). 2018;1825:413-424. Available from: https://doi.org/10.1007/978-1-4939-8639-2_14
  • Araki M, Okuno Y. “Molecular Mechanism of Resistance to Kinase Inhibitors Clarified by a Binding Free Energy Computation Method and Its Improvement by Incorporating Protein Flexibility” BIOPHYSICAL JOURNAL. 2018;114(3):56A. Available from: https://doi.org/10.1016/j.bpj.2017.11.361
  • Terayama K, Iwata H, Araki M, Okuno Y, Tsuda K. “Machine learning accelerates MD-based binding pose prediction between ligands and proteins” Bioinformatics. 2018;34(5):770-778. Available from: https://doi.org/10.1093/bioinformatics/btx638
  • Iwata Y, Katayama Y, Okuno Y, Wakabayashi S. “Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model” Oncotarget. 2018;9(18):14042-14057. Available from: https://doi.org/10.18632/oncotarget.24449
  • Kondo T, Kanai M, Kou T, Sakuma T, Mochizuki H, Kamada M, Nakatsui M, Uza N, Kodama Y, Masui T, Takaori K, Matsumoto S, Miyake H, Okuno Y, Muto M. “Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer” Oncotarget. 2018;9(28):19817-19825. Available from: https://doi.org/10.18632/oncotarget.24865
  • Noda Y, Kuzuya A, Tanigawa K, Araki M, Kawai R, Ma B, Sasakura Y, Maesako M, Tashiro Y, Miyamoto M, Uemura K, Okuno Y, Kinoshita A. “Fibronectin type Ⅲ domain-containing protein 5 interacts with APP and decreases amyloid β production in Alzheimer’s disease” Molecular Brain. 2018;11:61. Available from: https://doi.org/10.1186/s13041-018-0401-8
  • Araki M, Iwata H, Ma B, Fujita A, Terayama K, Sagae Y, Ono F, Tsuda K, Kamiya N, Okuno Y. “Improving the accuracy of protein-ligand binding mode prediction using a molecular dynamics-based pocket generation approach” J. Comput. Chem. 2018;39(32):2679-2689. Available from: https://doi.org/10.1002/jcc.25715
  • Terayama K, Yamashita T, Oguchi T, Tsuda K. “Fine-grained optimization method for crystal structure prediction” npj Computational Materials, 2018;4(32). Available from: https://doi.org/10.1038/s41524-018-0090-y
  • Tamada Y. “Memory Efficient Parallel Algorithm for Optimal DAG Structure Search using Direct Communication” Journal of Parallel and Distributed Computing”, 2018;119:27-35. Available from: https://doi.org/10.1016/j.jpdc.2018.03.011
  • Kawai R, Araki M, Yoshimura M, Kamiya N, Ono M, Saji H, Okuno Y. “Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods” ACS Chem. Neurosci 2018;9(5):957-966. Available from: https://doi.org/10.1021/acschemneuro.7b00389
  • Nakaoku T, Kohno T, Araki M, Niho S, Chauhan R, Knowles P.P,  Tsuchihara K, Matsumoto S, Shimada Y, Mimaki S, Ishii G, Ichikawa H, Nagatoishi S, Tsumoto K, Okuno Y, Yoh K, McDonald N.Q, Goto K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 2018;9(1):625. Available from: https://doi.org/10.1038/s41467-018-02994-7
• 2017)

  • Nakatsui M, Kamada M, Araki M, Okuno Y. “In silico drug discovery by supercomputer “K”” Nihon yakurigaku zasshi.Folia pharmacologica Japonica. 2017;149(6):281-287. Available from: https://doi.org/10.1254/fpj.149.281
  • Schneider G, Funatsu K, Okuno Y, Winkler D. “De novo Drug Design – Ye olde Scoring Problem Revisited” Molecular informatics. 2017;36(1-2). Available from: https://doi.org/10.1002/minf.201681031
  • Kondo T, Kanai M, Kou T, Sakuma T, Mochizuki H, Kamada M, Nakatsui M, Uza N, Kodama Y, Masui T, Takaori K, Matsumoto S, Miyake H, Okuno Y, Muto M. “Impact of BRCAness on the efficacy of oxaliplatin-based chemotherapy in patients with unresectable pancreatic cancer” JOURNAL OF CLINICAL ONCOLOGY. 2017;35(4). Available from: https://doi.org/10.1200/JCO.2017.35.4_suppl.250
  • Nakayama T, Imanaka Y, Okuno Y, Kato G, Kuroda T, Goto R, Tanaka S, Tamura H, Fukuhara S, Fukuma S, Muto M, Yanagita M, Yamamoto Y. “Analysis of the evidence-practice gap to facilitate proper medical care for the elderly: investigation, using databases, of utilization measures for National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB)” Environmental health and preventive medicine. 2017;22(1):51. Available from: https://doi.org/10.1186/s12199-017-0644-5
  • Morita K, Suzuki K, Maeda S, Matsuo A, Mitsuda Y, Tokushige C, Kashiwazaki G, Taniguchi J, Maeda R, Noura M, Hirata M, Kataoka T, Yano A, Yamada Y, Kiyose H, Tokumasu M, Matsuo H, Tanaka S, Okuno Y, Muto M, Naka K, Ito K, Kitamura T, Kaneda Y, Liu PP, Bando T, Adachi S, Sugiyama H, Kamikubo Y. “Genetic regulation of the RUNX transcription factor family has antitumor effects” The Journal of clinical investigation. 2017;127(7):2815-2828. Available from: https://doi.org/10.1172/JCI91788
  • Fujita K, Taneishi K, Inamoto T, Ishizuya Y, Takada S, Tsujihata M, Tanigawa G, Minato N, Nakazawa S, Takada T, Iwanishi T, Uemura M, Okuno Y, Azuma H, Norio N. “Adjuvant chemotherapy improves survival of patients with high-risk upper urinary tract urothelial carcinoma: a propensity score-matched analysis” BMC Urol. 2017;17(1):110. Available from: https://doi.org/10.1186/s12894-017-0305-4
  • Murakami R, Matsumura N, Brown, J.B, Higasa K, Tsutsumi T, Kamada M, Abou-Taleb H, Hosoe Y, Kitamura S, Yamaguchi K, Abiko K, Hamanishi J, Baba T, Koshiyama M, Okuno Y, Yamada R, Matsuda F, Konishi I, Mandai M. “Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks” Am J Pathol. 2017;187:2246-2258. Available from: https://doi.org/10.1016/j.ajpath.2017.06.012
  • Terayama, K., Iwata, H., Araki, M., Okuno, Y., Tsuda, K. “Machine Learning Accelerates MD-based Binding-Pose Prediction between Ligands and Proteins” Bioinformatics. 2018;34(5):770-778. Available from: https://doi.org/10.1093/bioinformatics/btx638
  • Uneno Y, Taneishi K, Kanai M, Okamoto K, Yamamoto Y, Yoshioka A, Hiramoto S, Nozaki A, Nishikawa Y, Yamaguchi D, Tomono T, Nakatsui M, Baba M, Morita T, Matsumoto S, Kuroda T, Okuno Y, Muto M. “Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study” PloS One. 2017;12(8). Available from: https://doi.org/10.1371/journal.pone.0183291
  • Kiyan, W., Ito, A., Nakagawa, Y., Mukai, S., Mori, K., Arai, T., Uchino E, Okuno Y, Kuroki, H. “Relationships Between Quantitative Pulse-Echo Ultrasound Parameters from the Superficial Zone of the Human Articular Cartilage and Changes in Surface Roughness, Collagen Content or Collagen Orientation Caused by Early Degeneration” Ultrasound.Med.Biol. 2017;43(8):1703-1715. Available from: https://doi.org/10.1016/j.ultrasmedbio.2017.03.015
  • Bekker G, Kamiya N, Araki M, Fukuda I, Okuno Y, Nakamura H. “Accurate prediction of complex structure and affinity for a flexible protein receptor and its inhibitor” J. Chem. Theory Comput., 2017;13(6):2389-2399. Available from: https://doi.org/10.1021/acs.jctc.6b01127
  • Kou T, Kanai M, Yamamoto Y, Kamada M, Nakatsui M, Sakuma T, Mochizuki H, Hiroshima A, Sugiyama A, Nakamura E, Miyake H, Minamiguchi S, Takaori K, Matsumoto S, Haga H, Seno H, Kosugi S, Okuno Y, Muto M. “Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors” Cancer Science, 2017;108(7):1440-1446. Available from: https://doi.org/10.1111/cas.13265
  • Uchibori K, Inase N, Araki M, Kamada M, Sato S, Okuno Y, Fujita N, Katayama R. “Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer” Nature Communications. 2017;8:14768. Available from: https://doi.org/10.1038/ncomms14768
  • Nakaoku T, Kohno T, Araki M, Niho S, Chauhan R, Knowles P.P,  Tsuchihara K, Matsumoto S, Shimada Y, Mimaki S, Ishii G, Ichikawa H, Nagatoishi S, Tsumoto K, Okuno Y, Yoh K, McDonald N.Q, Goto K. “A secondary RET mutation in the activation loop conferring resistance to vandetanib” Nature Communications, 2018;9(1):625. Available from: https://doi.org/10.1038/s41467-018-02994-7
• 2016)

  • Hamanaka M, Taneishi Kei, Iwata H, Ye J, Pei J, Hou J, Okuno, Y. “CGBVS-DNN: prediction of compound-protein Interactions based on deep learning” Mol. Inf. 2016;36(1-2). Available from: https://doi.org/10.1002/minf.201600045
  • Araki M, Kamiya N, Sato M, Nakatsui M, Hirokawa T, Okuno Y, “The effect of conformational flexibility on binding free energy estimation between kinases and their Inhibitors” Journal of Chemical Information and Modeling. 2016;56(12):2445-2456.Available from: https://doi.org/10.1021/acs.jcim.6b00398
  • Nishikawa Y, Kanai M, Narahara M, Tamon A, Brown J.B, Taneishi K, Nakatsui M, Okamoto K, Uneno Y, Yamaguchi D, Tomono T, Mori Y, Matsumoto S, Okuno Y, Muto M. “Association between UGT1A1*28*28 genotype and lung cancer in the Japanese population” Int. J. Clin. Oncol. 2016;22(2):269-273. Available from: https://doi.org/10.1007/s10147-016-1061-2
• 2015)

  • Kikuchi O, Ohashi S, Nakai Y, Nakagawa S, Matsuoka K, Kobunai T, Takechi T, Amanuma Y, Yoshioka M, Ida T, Yamamoto Y, Okuno Y, Miyamoto S, Nakagawa H, Matsubara K, Chiba T, Muto M. “Novel 5-fluorouracil-resistant human esophageal squamous cell carcinoma cells with dihydropyrimidine dehydrogenase overexpression” Am. J. Cancer Res. 2015;5(8):2431-2440. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4568778/
  • Kimura G, Kadoyama K, Brown J.B, Nakamura T, Miki I, Nishiguchi K, Sakaeda T, Okuno Y. “Antipsychotics-associated serious adverse events in children: An analysis of the FAERS database” Int. J. Med. Sci. 2015;12(2):135-140. Available from: https://doi.org/10.7150/ijms.10453
  • Kawasaki K, Kondoh E, Chigusa Y, Ujita M, Murakami R, Mogami H, Brown J.B, Okuno Y, Konishi I. “Reliable pre-eclampsia pathways based on multiple independent microarray data sets” Molecular Human Reproduction 2015;21(2):217-224. Available from: https://doi.org/10.1093/molehr/gau096

• More

Review etc.

• 「タンパク質の分子動力学シミュレーションに基づく創薬計算技術の開発とゲノム医療分野での応用研究 」
  荒木望嗣. SAR News No.45, Cutting Edge, p10-18. ,2023年10月1日

• 「 DNB 理論とその応用 」
  奥牧人, 山下洋史, 岡本有司,合原一幸. 生体の科学　,医学書院 ,74(2), 96-101. ,2023年4月

• 「 AIと高血圧診療 」
  糀谷泰彦, 小清水宏, 中村和貴, 奥野恭史, 日本臨牀　高血圧の最新診断・治療update,日本臨牀社 ,vol.81,p55-59 ,2023年1月

• 「 製剤処方設計AIの開発 」
  岩田浩明, 千葉峻太朗, 長谷川亜樹, 大田雅照, 奥野恭史, 月刊 PHARMSTAGE , 技術情報協会 , 11月号 , 2022年11月15日

• 「 クライオEM密度マップからのタンパク質ダイナミクス情報推定
  寺山 慧, 石田 祥一, 松本 篤幸, 奥野 恭史 , 生物工学会誌 , 100,11 , 599-602 , 2022年11月

• 「 AI・シミュレーションによる薬剤開発の迅速化」
  田中良尚, 松本篤幸, 奥野恭史, 実験医学 , 羊土社 , 40巻 13号 , 2122-2127 , 2022年10月1日

• 「深層学習技術を用いたクライオ電子顕微鏡データに潜むタンパク質運動性情報の抽出 」
  松本 篤幸, 寺山 慧, 奥野 恭史 , 生物物理 , 日本生物物理学会 , 62(3) , p193-197 , 2022年6月

• 「スーパーコンピュータ「富岳」によるCOVID-19治療薬探索」
  奧野恭史： 特集：特集：新興ウイルス感染症の早期予防・治療を目指して～COVID-19対策から考える～ . 日本薬理学雑誌 , 日本薬理学会 , 157 巻 2 号 , p111-114 , 2022年3月1日

• 「創薬におけるAIの活用」
  奧野恭史 , 先進医療 Navigator 『医療とAI最前線』, 日本医学出版, p58,-60,2022年2月

• 「薬として使えそうな物質をAIが提案」
  奧野恭史 , ニュートン別冊『ゼロからわかる人口知能 完全版 , p56,-57, 2022年2月

• 「薬として使えそうな物質をAIが提案」
  奧野恭史： ニュートン別冊『ゼロからわかる人口知能 増補第2版 , 2022.1

• 「日本人疾患バリアントデータベース MgeND」
  鎌田真由美,芦根怜,峰晴陽平,奧野恭史, 遺伝子医学39号,株式会社メディカルドゥ, Vol12 No1, p65-69,2022年1月1日発行

• 「これまでの創薬の”常識”をくつがえす新薬たち」
  奧野恭史： ニュートン別冊『くすりの科学知識 , 改訂第3版 , 74-76 , 2021.11

• 「AIによるAKI発症予測」
  櫻木実, 内野詠一郎, 佐藤憲明, 奧野恭史, 柳田素子： 腎臓内科 , 科学評論社 , 第14巻第4, 2021.10

• 「協調的2アミノ酸残基同時変異体の相互作用解析による新規抗体のStructure-Based Design」
  千葉峻太朗, 大田雅照： 医学のあゆみ , 278巻6号 , 2021.8

• 「ライフインテリジェンスコンソーシアム(LINC)」
  奥野恭史：医学のあゆみ , 278巻6号 , 637-640, 2021.8

• 「医薬品開発におけるAI活用」
  岩田浩明，奥野恭史：検査と技術, 第49巻, 第7号, 766-769, 2021.7.1

• 「日本人疾患ゲノム情報統合データベースMGeND」
  鎌田真由美，中津井雅彦，奥野恭史：実験医学増刊, Vol. 39, No.7, 90-96, 2021.5.1

• 「AIと創薬」
  奥野恭史：小児外科, Vol.53, No.4, 378-381, 2021.4.25

• 「ビッグデータを用いた創薬・医科学の最前線」
  奥野恭史：學士會会報, March Vol. 974, 2021-II, 104-109, 2021.3

• 「「富岳」で飛躍するコンピュータ創薬」
  奥野恭史：医学のあゆみ　Vol. 276, No.9 , 837-841, 2021.2.27

• 「人工知能の利活用-診断・治療支援を高度化する予測医療」
  奥野恭史：特集Digital Hypertension, Medical Science Digest, Vol 47 (1), 25-28, 2021.

• 「医療における人工知能「AI」」
  植田彰彦,小島諒介,山口建,濱西潤三,万代昌紀,奥野恭史：産婦人科の実際　69(5), 2020．

• 「データ主導型個別化医療と予測医療」
  奥野恭史,小清水宏,鎌田真由美,玉田嘉紀,小島諒介,内野詠一郎：あいみっく 41(1),2020．

• 「臨床データを用いた医療ビッグデータの解析と創薬への応用」
  内野詠一郎,佐藤憲明,小島諒介,玉田嘉紀,柳田素子,奥野恭史：日腎会誌, 62(2), 24-27，2020．

• 「臨床データからの創薬」
  佐藤憲明，内野詠一郎，奥野恭史：月刊細胞，51(7)，24-27，2019．

• 「創薬のためのタンパク質構造」
  大田雅照，池口満徳：月刊細胞　51(7)，20-23，2019．

• 「AIによる逆合成解析に向けて」
  寺山　慧，石田祥一，奥野恭史：月刊細胞　51(7)，12-15，2019

• 「標的分子探索，ドラッグリポジショニング，オミクスメカニズム解明」
  岩田浩明，小島諒介，玉田嘉紀：月刊細胞　51(7)，4-7，2019

• 「総論　第4次産業革命における創薬イノベーション」
  奥野恭史:月刊細胞　51(7)，2-3，2019.

• 「機械学習を用いた医療データ解析」
  佐藤憲明, 小島諒介，奥野恭史:Medical Science Digest 45(5)，23-26，2019.

• 「計算創薬におけるシミュレーション・機械学習・実験の融合に向けて」
  徳久淳師, 寺山　慧, 奥野恭史:分子シミュレーション学会誌「アンサンブル」21(2), 115-125, 2019.

• 「ビッグデータ創薬」
  奥野恭史:「ビッグデータ創薬」最新医学　74(3),62-66,2019.

• 「医療ｘAI－AIの基礎と医療への応用―」
  玉田嘉紀, 佐藤憲明, 奥野恭史: 整形・災害外科　62，215-221，2019.

• 「囲碁AIから逆合成解析へ−情報科学からのアプローチ」
  寺山　慧, 石田祥一, 奥野恭史：化学 74(2), 36-40, 2019.2

• 「特集：AIと創薬ーコンピュータ支援有機合成の現在」
  松原誠二郎, 寺山　慧, 奥野恭史：MEDCHEM NEWS　28(4), 181-186, 2018.

• 「非小細胞肺がん（NSCLC）における薬剤耐性獲得メカニズムの予測」
  荒木望嗣, 奥野恭史：がん分子標的治療　16(3), 53-58, 2018.

• 「疾患レジストリーと知識データベース」
  奥野恭史, 中津井雅彦, 鎌田真由美：日本医師会雑誌 第147巻第7号, 1395-1399, 2018.10.1

• 「がんゲノム医療用知識データベース」
  中津井雅彦, 鎌田真由美, 奥野恭史：実験医学　増刊 36(15), 36-40, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  ７. データベース・計算環境～知識データベース・AI基盤
  長尾知生子, 種石　慶：医薬ジャーナル　54(9), 113-117, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  6.  バイオメディカル・基礎から臨床への開発プロセス（２）
  　3）治験・市販後・メディカルアフェアーズ
  中津井雅彦, 飯塚博美, 新村直哉ほか：医薬ジャーナル　54(9), 109-112, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  6. バイオメディカル・基礎から臨床への開発プロセス（２）
  ２）バイオロジクス・製剤・ロボティクス～医薬品製造・バイオロジクス生産に向けたAI技術開発～
  荒木通啓：医薬ジャーナル　54(9), 105-108, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  3.  診断・治療～臨床・診断
  鎌田真由美：医薬ジャーナル　54(9), 73-77, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  ２．予防・先制医療における疾患発症予測モデルと細菌叢
  内野詠一郎, 佐藤憲明, 奥野恭史：医薬ジャーナル　54(9), 69-72, 2018.

• 特集「人工知能（ＡＩ）がもたらす創薬イノベーション」
  序文  ～LINCの設立とAI創薬～
  奥野恭史, 水口賢司, 本間光貴：医薬ジャーナル　54(9), 65-67, 2018.

• 「ビッグデータ時代のゲノム創薬」
  内野詠一郎、荒木通啓、奥野恭史：分子精神医学 2018.7

• 「次世代創薬シミュレーション」
  荒木望嗣, 奥野恭史：生体の科学, 金原一郎記念医学医療振興財団, 69(4), 310-314, 2018.

• 「最先端研究：ビッグデータ解析と分子シミュレーションを活用したインシリコ創薬」
  千葉峻太朗, 荒木望嗣, 奥野恭史：シミュレーション　37(1), 42-49, 2018.

• 「AIを用いた創薬の実際と今後の展望」
  小島諒介, 奥野恭史：INNERVISION　33(7), 71-73, 2018.

• 「集合知によるAI創薬の展開‐ライフインテリジェンスコンソーシアム（LINC）」
  荒木通啓, 中津井正彦, 奥野恭史：国際医薬品情報 1106, 16-22, 2018.

• 「スーパーコンピュータ・人工知能によるインシリコ創薬」
  岩田浩明, 荒木望嗣, 奥野恭史：化学工業　69(5), 58-66, 2018.

• 「人工知能を用いた創薬（AI創薬）」
  藤原　大, 鎌田真由美, 奥野恭史：癌と化学療法　45(4), 593-596, 2018.

• 「AIによる腎生検組織画像の診断支援システム開発」
  内野詠一郎, 佐藤憲明, 奥野恭史, 柳田素子：腎臓　40(115), 15-18, 2018.

• 「ビッグデータの創薬への応用」
  種石　慶, 徳久淳師, 奥野恭史：EPSホールディングス「遥か」Vol.11, 2018

• 「スーパーコンピュータが可能にする医療と創薬」
  内野詠一郎, 中津井雅彦, 奥野恭史：月刊腎臓内科・泌尿器科 7(2), 160-164, 2018.

• テレメディシン「遠隔医療の現状と課題：遠隔医療の実現とビッグデータ」
  佐藤憲明, 岩田浩明, 奥野恭史：医学のあゆみ, 医歯薬出版　264(7), 2018.

• 人工知能と医療のハーモニー「人工知能を用いたビッグデータ創薬」
  藤原　大, 中津井雅彦, 奥野恭史：医学のあゆみ, 医歯薬出版 263(8), 2018.

• 「臨床ビッグデータ解析の展望―実臨床データとゲノム情報への応用」
  内野詠一郎, 種石　慶, 中津井雅彦, 鎌田真由美, 荒木望嗣, 奥野恭史：生体医工学　55(4):173-182, 2017.

• 「創薬におけるAIの可能性」
  種石　慶, 岩田浩明, 小島諒介, 奥野恭史：日本化学会情報化学部会誌, 35(3), 212, 2017.

• 「Precision Medicine実現に必要なシミュレーション科学とデータ科学（data centric science）」
  佐藤憲明, 内野詠一郎, 鎌田真由美, 奥野恭史：腫瘍内科 20(4), 306-312, 2017.

• 「医療ビッグデータ解析による実臨床からの生命科学展開」
  中津井雅彦, 種石　慶, 奥野恭史：実験医学 35(1), 2017.

• 「スパコン「京」によるインシリコ創薬」
  中津井雅彦, 鎌田真由美, 荒木望嗣, 奥野恭史：日本薬理学雑誌 149(6), 281-287, 2017.

• 「網羅的癌関連遺伝子変異検査（OncoPrime^TM）による膵癌ゲノム異常解析と治療への応用」
  金井雅史, 高忠之, 松本繁巳, 武藤学, 児玉裕三, 増井俊彦, 高折恭一, 南口早智子, 鎌田真由美, 中津井雅彦, 奥野恭史, 佐久間朋寛, 望月洋明, 広島明宣： 胆と膵 37 9月号　医学図書出版（株）, 2016.

• 「生体分子システムの機能制御による革新的創薬基盤の構築」
  奥野恭史, BioSupercomputing Newsletter 13, 12-13, 2015.

• 「神戸インシリコ創薬拠点の形成について」
  奥野恭史,都市政策,160,20-26,2015

• 「スパコン創薬から観えるインシリコ創薬の未来」
  奥野恭史,薬剤学,74(5),327-334,2014

• “Compound-protein interaction prediction within chemogenomics: theoretical concepts, practical usage, and future directions“
  Brown, J.B., Niijima, S., Okuno, Y.,Molecular Informatics,32,906–921,2013

• 「スーパーコンピュータ「京」が拓くコンピュータ創薬の未来」
  中津井雅彦, 奥野恭史,情報処理,55(8),836-841,2014.

• 「スーパーコンピュータ「京」が拓くコンピュータ創薬の未来」
  奥野恭史,ファルマシア,50(5),433-437,2014.

• 「コンピュータ創薬の基盤となる計算化学分野の受賞」
  奥野恭史,医学のあゆみ,247,2013

• “Data Mining of the Public Version of the FDA Adverse Event Reporting System”
  Sakaeda, T., Tamon, A., Kadoyama, K., Okuno, Y., Int.J.Med.Sci., 10(7), 796-803, 2013

• 「ケモセントリックアプローチ」
  新島　聡, CICSJ Bulletin, 30(4), 61-64, 2012

• 「ケモゲノミクス：ゲノムからケミカルスペースへ」
  奥野恭史, CICSJ Bulletin, 30(4), 60, 2012

• 「医薬品有害事象データベースを用いたデータマイニング」
  五島　誠, 奥野恭史, SAR News, 23, 12-18, 2012

• “Staying grounded in reality – an opinion on why informatics students should have laboratory experience“
  Brown, J.B., Mol. Inf. , 31(11-12):777-781, 2012

• “Systems biology and systems chemistry: new directions for drug discovery“
  Brown, J.B., Okuno, Y., Chem Biol, 19(1), 23-8, 2012

• “Unifying Bioinformatics and Chemoinformatics for Drug Design”
  Brown, J.B., Okuno, Y., Systems and Computational Biology — Bioinformatics and Computational Modeling, InTech, 99-120, 2011

• 「医薬品による有害事象の自発報告システム」
  栄田敏之, 角山香織, 奥野恭史, 人工知能学会誌, 26(2), 126-130, 2011

• 「創薬バリューチェインのインシリコ技術を活用した阻害剤開発の試み」
  井上　豪, 安達宏昭, 森　勇介, 高野和文, 松村浩由, 村上　聡, 福西快文, 中村春木, 木下誉富, 仲西　功, 奥野恭史, 南方聖司, 佐久間俊広, 高田俊和, 北島正人, 福岡良忠, 坂田恒昭, 日本結晶学会誌, 52, 89-94, 2010

• 「化合物-タンパク質活性空間における特徴選択」
  新島　聡, 奥野恭史, Technical Reports of the 12th Workshop on Information-Based Induction Sciences (IBIS2009), 37, 2009

• “Biogenesis and Function Mechanisms of Micro-RNAs and Their Role as Oncogenes and Tumor Suppressors”
  Tsuchiya, S., Terasawa, K., Kunimoto, R., Okuno, Y., Sato, F., Shimizu, K. and Tsujimoto, G., Systems Biology, Springer, 183-189, 2009

• “New Progress in Crystallization Technology of Membrane Protein and Introduction of Pharamaceutical Innovation Value Chain”
  Inoue, T., Adachi, H., Murakami, S., Takano, K., Matsumura, H., Mori, Y., Fukunishi, Y., Nakamura, H., Kinoshita, T., Nakanishi, I., Okuno, Y., Minakata, S., Shimojo, S., Sakata, T., YAKUGAKU ZASSHI, 128(4), 497-505, 2008

• 「ケミカルゲノミクス情報を用いた新規リガンド探索手法」
  新島　聡, 奥野恭史, 日本薬理学雑誌, 133:3, 173-174, 2009

• 「ケミカル・バイオ情報に基づく創薬インフォマティクス研究」
  奥野恭史, Pharma VISION NEWS, 9, 13-16, 2007

Book

• 『プレシジョン・メディシンを加速する「創薬ビッグデータ統合システム」の推進』
  荒木望嗣、鎌田真由美、奥野恭史.「富岳」が拓く計算科学の未来, milsil, 2020年11月1日発行.

• 『第1章第1節「ゲノム解析の概要と現状」』
  鎌田真由美, 奥野恭史. 「医薬品開発におけるオミクス解析技術～ゲノム・トランスクリプトーム・プロテオーム・メタボローム～」, 情報機構, 2020年3月発行.

• 『スーパーコンピュータと創薬』
  奥野恭史. 高校生向け図集「サイエンスビュー　化学総合資料」P296-297, 実教出版. 2019年3月10日発行.

• 『AI導入によるバイオテクノロジーの発展』
  徳久淳師、種石　慶、奥野恭史：「創薬におけるビッグデータの可能性」,シーエムシー出版(2018).

• 『in silico創薬におけるスクリーニングの高速化・高精度化技術』
  徳久淳師、金田亮、岩田浩明、馬彪、井阪悠太、奥野恭史（担当項目にて共著）. 技術情報協会 (2018)

• 『日本人のためのゲノム医療用AI、2018年度中に試行開始』
  鎌田真由美、奥野恭史. 週刊医学界新聞、第3254号（2018.1.1）

• 実験医学別冊  『あなたのラボにAI（人工知能）×ロボットがやってくる―研究に生産性と創造性をもたらすテクノロジー』
  夏目　徹／編．種石　慶，岩田浩明，小島諒介，奥野恭史／著「創薬とAIの良好な関係」（2017）

• 『変貌する医療市場』研究・技術革新・社会実装
  奥野恭史（共著）. 監修：木村廣道.　かんき出版　(2017)

• 『人工知能・機械学習・ディープラーニング関連技術とその活用』
  岩田浩明, 種石慶, 奥野恭史　共著「創薬と人工知能」　(株)情報機構　(2016)

• 『ビッグデータの収集、調査、分析と活用事例』
  奥野恭史　共著　技術情報協会　(2014.11.28)

• 『スーパーコンピュータ「京」によるビッグデータ創薬』
  奥野恭史共著「ビッグデータの収集、調査、分析と活用事例」,(株)技術情報協会　(2014)

• 『生命のビッグデータ利用の最前線』
  奥野恭史共著,シーエムシー出版(2014)

• 『化学便覧 応用化学編 第7版』
  奥野恭史共著, 丸善出版(株)(2014)

• 『疾患克服をめざしたケミカルバイオロジー（実験医学増刊）』
  奥野恭史共著　,㈱羊土社

• 『シミュレーション辞典』
  奥野恭史　共著, ㈱コロナ社(2012)

• 『最新 創薬インフォマティクス活用マニュアル』
  奥野恭史編集, (株)メディカルドゥ(2011)

• 『Handbook of Systems Toxicology』
  奥野恭史　共著, Wiley-Blackwell(2011)

• 『薬学の展望とロードマップ』
  奥野恭史　共著, 日本薬学会(2010)

• 『医薬ジャーナル　増刊号 新薬展望2010』
  奥野恭史　共著, ㈱医薬ジャーナル社(2010)

• 『次世代創薬テクノロジー 実践：インシリコ創薬の最前線』
  奥野恭史　共著, ㈱メディカル・ドゥ(2009)

• 『コンピューターで薬を創ろう』
  奥野恭史　共著, 化学同人(2009)

• 『インシリコ創薬科学 －ゲノム情報から創薬へ－』
  奥野恭史　共著･編集, 京都廣川書店(2008)

Patent

• 特願2021-066780
  「長時間分子動力学シミュレーションによる変異型タンパク質に対する治療薬探索法 」

  発明者：奥野恭史, 荒木望嗣, 片山量平

   

• 特願K20210055
  「未熟児網膜症の信仰予測装置、予測方法 」

  発明者：福嶋葉子、奥野恭史、小島諒介

   

• 特願2021-092251
  「予測装置、予測方法、予測プログラム、予測システム」

  発明者：西真宏, 的場聖明, 奥野恭史, 内野詠一郎

   

• 特願2020-178148
  「健康改善経路探索装及び健康改善経路探索方法」

  発明者：奥野恭史, 小島諒介, 内野詠一郎, 中路重之, 伊東健, 村下公一, 中村和貴

   

• 特願2020-002923
  「特徴ネットワーク抽出装置、コンピュータプログラム、特徴ネットワーク抽出方法及びベイジアンネットワーク分析方法 」

  発明者：奥野恭史, 玉田嘉紀

   

• 特願201780066425.4
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願17867434.7
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2007-53322
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2018-549058
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司,松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願106137915
  「PD-1シグナル阻害剤による疾患治療における有効性判定マーカー」

  発明者：本庶 佑, 茶本健司, 松田文彦, ファガラサン・シドニア, 奥野恭史

   

• 特願2014-532989 5946045, 6750177
  「化合物設計装置,化合物設計方法,及びコンピュータプログラム 」

  発明者：奥野恭史, 金井千里, 吉川達也, 多門啓子

   

• 特願2007-53322
  「ケミカルゲノム情報に基づく、タンパク質－化合物相互作用の予測と化合物ライブラリーの合理的設計 」

  発明者：奥野恭史, 種石慶, 辻本豪三

   

• 特願2007-53322
  「マイクロＲＮＡ標的遺伝子予測装置」

  発明者：奥野恭史, 辻本豪三, 国本亮, 寺澤和哉, 土屋創健, 秋山英雄, 妙本明

   

• 特願2016-074085
  「アントラニルアミド誘導体およびそれを含有するTLR3が関与する疾患の治療剤」

  発明者：奥野恭史, 木下　茂, 土黒一郎, 上田真由美